import torch
import torch.nn as nn
import torch.nn.functional as F
from math import exp


def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
    return gauss/gauss.sum()


def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()

    return window


def _ssim(img1, img2, window, window_size, channel, size_average = True):
    mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
    mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1*mu2

    sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq
    sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq
    sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2

    C1 = 0.01**2
    C2 = 0.03**2

    ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)


class SSIM_loss(nn.Module):
    def __init__(self, window_size, channel, size_average=True):
        super(SSIM_loss, self).__init__()
        self.window_size = window_size
        self.channel = channel
        self.size_average = size_average
        self.window = create_window(window_size, self.channel)

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.data.type() == img1.data.type():
            window = self.window
        else:
            window = create_window(self.window_size, channel)

            if img1.is_cuda:
                window = window.cuda(img1.get_device())
            window = window.type_as(img1)

            self.window = window
            self.channel = channel
        loss = -_ssim(img1, img2, window, self.window_size, channel, self.size_average)

        return loss


class Multi_SSIM_loss(nn.Module):
    def __init__(self, window_sizes, channel, size_average=True):
        super(Multi_SSIM_loss, self).__init__()
        self.losses = list()
        for size in window_sizes:
            self.losses.append(SSIM_loss(size, channel, size_average))
        self.losses = nn.ModuleList(self.losses)

    def forward(self, img1, img2):
        loss_multi = list()
        for i in range(len(self.losses)):
            loss_multi.append(self.losses[i](img1, img2))

        return loss_multi
